forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Reduce.h
314 lines (288 loc) · 11.9 KB
/
Reduce.h
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
#pragma once
#include <ATen/native/cpu/Loops.h>
#include <ATen/Parallel.h>
#include <c10/util/TypeList.h>
#include <c10/core/Scalar.h>
#include <c10/util/irange.h>
#include <sstream>
#include <type_traits>
namespace at { namespace native { inline namespace CPU_CAPABILITY {
using namespace vec;
#define VEC_LOOP_HEADER(func_t, data) \
using scalar_t = typename function_traits<func_t>::result_type; \
using Vec = Vectorized<scalar_t>; \
char* out_ptr = data[0]; \
(void) out_ptr;
// reduction that is contiguous over the input in dim 0
template <typename traits>
static inline bool is_contiguous_reduction(const int64_t* strides) {
return strides[0] == 0 &&
strides[1] == sizeof(typename traits::arg2_t);
}
// reduction that is contiguous over the input in dim 1
template <typename traits>
static inline bool is_outer_reduction(const int64_t* strides) {
return strides[0] == 0 &&
strides[2] == sizeof(typename traits::result_type) &&
strides[3] == sizeof(typename traits::arg2_t);
}
template <typename func_t, typename vec_func_t>
static inline void vectorized_reduction(char** data, int64_t n, int64_t stride,
func_t op, vec_func_t vop, bool reduce) {
VEC_LOOP_HEADER(func_t, data)
const char* in1_ptr = data[1];
Vec acc[4];
for (const auto j : c10::irange(4)) {
acc[j] = Vec::loadu(in1_ptr + j * Vec::size() * sizeof(scalar_t));
}
for (const auto i : c10::irange(1, n)) {
const char* ptr = in1_ptr + stride * i;
acc[0] = vop(acc[0], Vec::loadu(ptr + (0 * Vec::size() * sizeof(scalar_t))));
acc[1] = vop(acc[1], Vec::loadu(ptr + (1 * Vec::size() * sizeof(scalar_t))));
acc[2] = vop(acc[2], Vec::loadu(ptr + (2 * Vec::size() * sizeof(scalar_t))));
acc[3] = vop(acc[3], Vec::loadu(ptr + (3 * Vec::size() * sizeof(scalar_t))));
}
if (reduce) {
scalar_t buffer[Vec::size()];
acc[0] = vop(vop(acc[0], acc[1]), vop(acc[2], acc[3]));
acc[0].store(buffer);
for (const auto j : c10::irange(1, Vec::size())) {
buffer[0] = op(buffer[0], buffer[j]);
}
auto dst = (scalar_t*)out_ptr;
*dst = op(*dst, buffer[0]);
} else {
for (const auto j : c10::irange(4)) {
auto dst = out_ptr + j * Vec::size() * sizeof(scalar_t);
acc[j] = vop(acc[j], Vec::loadu(dst));
acc[j].store(dst);
}
}
}
template <typename F>
static inline void UNARY_OUTER_LOOP(char* data[2], const int64_t strides[2], int64_t n, F f) {
for (const auto j C10_UNUSED : c10::irange(n)) {
f();
data[0] += strides[0];
data[1] += strides[1];
}
}
// computes the reduction out = op(out, in)
template <typename func_t, typename vec_func_t>
static inline void vectorized_inner_reduction(char** data, int64_t n, func_t op, vec_func_t vop) {
VEC_LOOP_HEADER(func_t, data)
int64_t vector_stride = 4 * Vec::size() * sizeof(scalar_t);
int64_t count = n / (4 * Vec::size());
if (count > 0) {
vectorized_reduction(data, count, vector_stride, op, vop, /*reduce=*/true);
}
char* ptrs[3] = { data[0], data[0], data[1] };
int64_t strides[] = { 0, 0, sizeof(scalar_t) };
basic_loop(ptrs, strides, count * 4 * Vec::size(), n, op);
}
// computes the reduction out = op(out, in)
template <typename func_t, typename vec_func_t>
static inline void vectorized_outer_reduction(char** data, int64_t inner_stride, int64_t size0, int64_t size1, func_t op, vec_func_t vop) {
VEC_LOOP_HEADER(func_t, data)
// reduce down each column of 4 * Vec::size() elements (128 or 256 bytes)
#if defined(CPU_CAPABILITY_AVX512)
int64_t outer_stride[2] = { 256, 256 };
#else
int64_t outer_stride[2] = { 128, 128 };
#endif
UNARY_OUTER_LOOP(data, outer_stride, size1 / (4 * Vec::size()), [&] {
vectorized_reduction(data, size0, inner_stride, op, vop, /*reduce=*/false);
});
// reduce down the remaining columns
int64_t step[] = { sizeof(scalar_t), sizeof(scalar_t) };
int64_t remaining = size1 % (4 * Vec::size());
UNARY_OUTER_LOOP(data, step, remaining, [&] {
char* ptrs[3] = { data[0], data[0], data[1] };
int64_t strides[] = { 0, 0, inner_stride };
basic_loop(ptrs, strides, 0, size0, op);
});
}
template<typename traits, typename res_t>
static void set_result(const int index, const res_t result, const TensorIteratorBase &iter, const int num_outputs) {
// static_assert(std::is_same<res_t, typename traits::arg2_t>::value, "data types must match");
if (index < num_outputs) {
char *out = (char *) iter.data_ptr(index);
*(res_t *) out = result;
}
}
template<typename traits, typename res_t>
static void set_results(const res_t result, const TensorIteratorBase &iter, const int num_outputs) {
AT_ASSERT(num_outputs == 1);
set_result<traits>(0, result, iter, num_outputs);
}
template<typename traits, std::size_t i = 0, typename... tuple_t>
static inline typename std::enable_if<i == sizeof...(tuple_t), std::size_t>::type
for_each_in_tuple(const std::tuple<tuple_t...>& /*t*/, const TensorIteratorBase& /*iter*/, const int /*num_outputs*/) {
return i;
}
template<typename traits, std::size_t i = 0, typename... tuple_t>
static inline typename std::enable_if<i < sizeof...(tuple_t), std::size_t>::type
for_each_in_tuple(const std::tuple<tuple_t...>& t, const TensorIteratorBase &iter, const int num_outputs) {
if (i < (size_t)num_outputs) {
set_result<traits>(i, std::get<i>(t), iter, num_outputs);
return for_each_in_tuple<traits, i + 1, tuple_t...>(t, iter, num_outputs);
}
return i;
}
template<typename traits, typename... res_t>
static void set_results(const std::tuple<res_t...>& result, const TensorIteratorBase &iter, const int num_outputs) {
AT_ASSERT(num_outputs >= 1);
std::size_t result_size = for_each_in_tuple<traits>(result, iter, num_outputs);
AT_ASSERT((size_t)num_outputs == result_size);
}
template <typename T, typename... Args>
struct all_same : std::conjunction<
std::is_same<T, Args>...
> {};
// data_t is the input/output data type.
// acc_t is a type that contains all the necessary data
// to continue reducing.
// index_t is a one-dimensional index
//
// ops_t is such that &ops_t::reduce, &ops_t::combine, and &ops_t::project exist and satisfy
// the following.
// reduce: (acc_t, data_t, index_t) -> acc_t adds one data point to the accumulated value.
// combine: (acc_t, acc_t) -> acc_t combines two accumulated values into one.
// project: acc_t -> out_t finishes the reduction, getting the required output.
//
// Additionally, acc_t must be default-constructible:
// acc_t {} is an identity for combine,
// and project(acc_t {}) is the value of the operation on zero elements.
//
// The point of `combine` is to support parallelization -
// the idea is to one sequence of `reduce` calls per thread of execution,
// and then to combine them at the end with `combine`.
//
// If there is more than one output element,
// our parallelization strategy is to use one thread for each of them,
// which means that `combine` will never be called.
//
// If, on the other hand, there is only one, then we split the input into
// into several pieces, reduce each separately, and then combine them.
template <typename ops_t, typename init_t>
void binary_kernel_reduce(TensorIteratorBase& iter, ops_t ops, init_t init) {
using rf_t = decltype(&ops_t::reduce);
using cf_t = decltype(&ops_t::combine);
using pf_t = decltype(&ops_t::project);
using r_traits = binary_function_traits<rf_t>;
using c_traits = binary_function_traits<cf_t>;
using p_traits = unary_function_traits<pf_t>;
using acc_t = typename p_traits::arg1_t;
using data_t = typename r_traits::arg2_t;
static_assert(
all_same<
acc_t,
init_t,
typename r_traits::arg1_t,
typename r_traits::result_type,
typename c_traits::arg1_t,
typename c_traits::arg2_t,
typename c_traits::result_type>::value,
"all accumulate types must match");
static_assert(
std::is_default_constructible<acc_t>::value,
"the accumulate type must be default-constructible"
);
const int num_outputs = iter.noutputs();
iter.foreach_reduced_elt([&ops, &init, num_outputs](TensorIteratorBase &sub_iter) {
auto reduction_body = [&ops, &sub_iter, num_outputs](acc_t acc, int64_t begin, int64_t end) -> acc_t {
int ntensors = sub_iter.ntensors();
sub_iter.serial_for_each([&acc, &ops, num_outputs, ntensors, begin](char** data, const int64_t* strides, int64_t size) {
AT_ASSERT(ntensors - num_outputs == 1);
char *in = data[ntensors - 1];
int64_t stride = strides[ntensors - 1];
for (const auto i : c10::irange(size)) {
acc = ops.reduce(acc, c10::load<data_t>(in), begin + i);
in += stride;
}
}, {begin, end});
return ops.translate_idx(acc, sub_iter.view_offsets()[0]);
};
acc_t total_acc = init;
auto numel = sub_iter.numel();
if (numel < at::internal::GRAIN_SIZE || at::get_num_threads() == 1 ||
at::in_parallel_region()) {
total_acc = reduction_body(total_acc, 0, numel);
} else {
int max_threads = at::get_num_threads();
AT_ASSERT(max_threads > 0);
static_assert(
!std::is_same<acc_t, bool>::value,
"Concurrently modifying different references into std::vector<bool> is UB."
);
std::vector<acc_t> buffer((unsigned)max_threads, init);
at::parallel_for(0, numel, internal::GRAIN_SIZE,
[&](int64_t begin, int64_t end) {
auto& acc = buffer[at::get_thread_num()];
acc = reduction_body(acc, begin, end);
}
);
for (const auto i : c10::irange(max_threads)) {
total_acc = ops.combine(total_acc, buffer[i]);
}
}
set_results<r_traits>(ops.project(total_acc), sub_iter, num_outputs);
});
}
template <typename func_t, typename vec_func_t>
void binary_kernel_reduce_vec(TensorIteratorBase& iter, func_t op, vec_func_t vop, double ident = 0) {
using traits = binary_function_traits<func_t>;
static_assert(
all_same<
typename traits::result_type,
typename traits::arg1_t,
typename traits::arg2_t>::value,
"all types must match");
iter.output_base().fill_(ident);
iter.parallel_reduce([&](char** data, const int64_t* strides, int64_t size0, int64_t size1) {
int64_t outer_strides[] = { strides[2], strides[3] };
if (is_contiguous_reduction<traits>(strides)) {
// input is contiguous in dim 0, output is reduced in dim 0
UNARY_OUTER_LOOP(data, outer_strides, size1, [&] {
vectorized_inner_reduction(data, size0, op, vop);
});
} else if (is_outer_reduction<traits>(strides)) {
// input and output are contiguous in dim 1
int64_t inner_stride = strides[1]; // stride of input in dim 0
vectorized_outer_reduction(data, inner_stride, size0, size1, op, vop);
} else {
UNARY_OUTER_LOOP(data, outer_strides, size1, [&] {
char* ptrs[3] = { data[0], data[0], data[1] };
int64_t inner_strides[3] = { strides[0], strides[0], strides[1] };
basic_loop(ptrs, inner_strides, 0, size0, op);
});
}
});
}
// when reduction is on most inner dimension (dim 0 in TensorIterator)
// and input has contiguous most inner dimension, `binary_kernel_reduce_lastdim`
// can be used.
static inline bool is_reduce_lastdim(TensorIteratorBase& iter) {
return iter.num_reduce_dims() == 1 && iter.is_dim_reduced(0)
&& iter.ninputs() == 1 && iter.strides(1)[0] == iter.element_size(1);
}
template <typename reduce_func_t>
void binary_kernel_reduce_lastdim(TensorIteratorBase& iter, reduce_func_t reduce_op) {
auto shape = iter.shape();
int64_t dim_size = shape[0];
int64_t grain_size = std::max((int64_t) 1, at::internal::GRAIN_SIZE / dim_size);
TensorIterator sub_iter(iter);
// create sub iterator to parallel on all non-reduce-dims
sub_iter.narrow(0, 0, 1);
auto loop = [&](char** data, const int64_t* strides, int64_t size) {
char* out = data[0];
char* in = data[1];
for (int64_t i = 0; i < size; ++i) {
reduce_op(out, in, dim_size);
out += strides[0];
in += strides[1];
}
};
sub_iter.for_each(loop, grain_size);
}
}}} // namespace at::native::<anonymous>